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One of the most important parameters in designing of sewer structures is the ability to accurately simulation the discharge and velocity field of them. Among the various sewer receiving inflow methods, open channel junctions are mostly occurring. Because of the separation and contraction zone that occur at the open channel junctions, the fluid flow has a complex behavior. Modeling is carried out by Radial Basis Function (RBF) neural network, Gene Expression Programming (GEP), and Multiple Non-Linear Regression (MNLR) methods. Finding the optimum situation for GEP and RBF models are done by examining the various mathematical and linking functions for GEP and different number of hidden neurons and spread amount for RBF. In order to use the models in practical situations, three equations were conducted by using the RBF, GEP, and MNLR methods in modeling the longitudinal velocity. Then, the surface integral of the presented equations is used to simulate the flow discharge. The results showed that the GEP and RBF method perform significantly better than the MNLR in open channel junction characteristics simulations. The GEP method has higher performance in modeling the longitudinal velocity field compare with the RBF. However, the RBF presented more reliable results on the discharge simulations.

Discharge and flow field simulation of open channel sewer junction using artificial intelligence methods